Hi,

I am confused on how to setup the afni_proc.py script to analyze my fMRI task about listening to story. In my experiment, the subjects are instructed to listen to two four-minute stories(one story, one run), and I want to make the correlation of two runs. But I encountered some problems in afni_proc.py script. This is my script:

afni_proc.py -subj_id sub9.c.demean.blur6 \
-dsets s9.1+orig.HEAD s9.4+orig.HEAD \
-blocks despike tshift align tlrc volreg blur mask scale regress \
-copy_anat anat+orig \
-align_opts_aea -deoblique on -cost lpc+ZZ \
-tlrc_base TT_N27+tlrc \
-volreg_align_to MIN_OUTLIER \
-volreg_align_e2a \
-volreg_tlrc_warp \
-blur_size 6.0 \
-regress_censor_motion 0.2 \
-regress_censor_outliers 0.1 \
-regress_motion_per_run \
-regress_apply_mot_types demean deriv \
-regress_est_blur_epits \
-regress_est_blur_errts \
-execute

I have two questions for your guidance:

First, in" -regress_censor_motion 0.2" and "regress_censor_outliers 0.1". in my experiment, I'm not sure whether I should use the censor in the afni_proc.py, because the censor out TR value is 0, This may affect voxel-by-voxel correlation.

Second, in" -regress_apply_mot_types ", I saw three options in the afni website," -regress_apply_mot_types basic", " -regress_apply_mot_types demean deriv " and " -regress_apply_mot_types deriv " . I've tried each of the three options and found they were different, I'm not sure which option to choose. My colleague gave me some advice, but we didn't reach a consistent result. So could someone give me a reasonable suggestion?

thanks a lot,
Dan